Transnational Private Environmental Rule Makers as Interest Organizations: Evidence from the European Union
Bibliographic record
Abstract
Abstract While anecdotal evidence suggests that transnational private rule-making organizations (TPROs)—such as eco-certification organizations—lobby public policy makers, we know little about the extent of this phenomenon or the characteristics of TPROs that lobby. TPRO lobbying is relevant given that their rule-making activities directly intersect with public policy. We use the interest group and private governance literatures to examine TPRO features that distinguish TPROs that lobby from those that do not. We developed an original data set of 147 environmental TPROs and assessed TPRO lobbying by their registration in the European Union’s Transparency Register (TR). We find that a quarter of the TPROs in our data set are registered in the TR, and that capacity and expertise matter. Contrary to expectations, however, we do not find that certain key features of TPROs—such as business origins or credibility—are correlated with being registered, which implies that these features do not create inequalities in the TPRO population in terms of lobbying likelihood. By assessing environmental TPROs as interest organizations that engage in lobbying, we contribute to research on public–private governance interactions and identify TPROs as an interest group population in its own right.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".